mlr3resampling (version 2026.2.24)

proj_compute: Compute resampling results in a project

Description

Runs train() and predict(), then a data table with one row is saved to an RDS file in the grid_jobs directory.

Usage

proj_compute(grid_job_i, proj_dir, verbose=FALSE, process_fun=Sys.getpid)

Value

proj_compute returns a data table with one row of results.

Arguments

grid_job_i

integer from 1 to number of jobs (rows in grid_jobs.csv).

proj_dir

Project directory created by proj_grid.

verbose

Logical: print messages?

process_fun

function called with no arguments (default Sys.getpid), should return integer process id number.

Author

Toby Dylan Hocking

Details

If everything goes well, the user should not need to run this function. Instead, the user runs proj_submit as Step 2 out of the typical 3 step pipeline (init grid, submit, read results). proj_compute can sometimes be useful for testing or debugging the submit step, since it runs one split at a time.

Examples

N <- 80
library(data.table)
set.seed(1)
reg.dt <- data.table(
  x=runif(N, -2, 2),
  person=factor(rep(c("Alice","Bob"), each=0.5*N)))
reg.pattern.list <- list(
  easy=function(x, person)x^2,
  impossible=function(x, person)(x^2)*(-1)^as.integer(person))
SOAK <- mlr3resampling::ResamplingSameOtherSizesCV$new()
reg.task.list <- list()
for(pattern in names(reg.pattern.list)){
  f <- reg.pattern.list[[pattern]]
  task.dt <- data.table(reg.dt)[
  , y := f(x,person)+rnorm(N, sd=0.5)
  ][]
  task.obj <- mlr3::TaskRegr$new(
    pattern, task.dt, target="y")
  task.obj$col_roles$feature <- "x"
  task.obj$col_roles$stratum <- "person"
  task.obj$col_roles$subset <- "person"
  reg.task.list[[pattern]] <- task.obj
}
reg.learner.list <- list(
  featureless=mlr3::LearnerRegrFeatureless$new())
if(requireNamespace("rpart")){
  reg.learner.list$rpart <- mlr3::LearnerRegrRpart$new()
}

pkg.proj.dir <- tempfile()
mlr3resampling::proj_grid(
  pkg.proj.dir,
  reg.task.list,
  reg.learner.list,
  SOAK,
  order_jobs = function(DT)1:2, # for CRAN.
  score_args=mlr3::msrs(c("regr.rmse", "regr.mae")))
mlr3resampling::proj_compute(1, pkg.proj.dir)